MLOps Engineering

Job Description

Are you a MlOps Engineer working at a Large Financial Institution and being told by your leadership that you are too hands-on or detail-oriented or think and work like a start-up?



Imagine working at Intellibus to engineer platforms that impact billions of lives around the world. With your passion and focus we will accomplish great things together



We are looking forward to you joining our MLops to Join Our Platform Engineering team to join our team and lead the implementation of MLOps practices within our organization and for our clients. The MLOps Consultant will play a critical role in bridging the gap between data science and IT operations by designing, implementing, and optimizing machine learning (ML) deployment pipelines and infrastructure. The ideal candidate will have a strong background in machine learning, software engineering, and DevOps practices, along with experience in deploying and managing ML models in production environments.



We are looking for Engineers who can



MLOps Strategy and Planning: Collaborate with stakeholders to define MLOps strategies aligned with business objectives and technical requirements. Assess current infrastructure, processes, and tooling to identify gaps and opportunities for MLOps implementation.
ML Pipeline Development: Design, develop, and implement end-to-end ML deployment pipelines for model training, validation, deployment, and monitoring. Automate data ingestion, feature engineering, model training, and evaluation processes using tools like Apache Airflow, Kubeflow, or MLflow.
Infrastructure Provisioning and Orchestration: Architect and deploy scalable infrastructure for ML workloads using cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes).
Implement infrastructure as code (IaC) practices for provisioning and managing ML infrastructure using tools like Terraform or AWS CloudFormation.
Model Deployment and Monitoring: Deploy ML models into production environments using containerized solutions and orchestration platforms.
Implement model monitoring and logging solutions to track model performance, data drift, and model drift in production.
Continuous Integration and Deployment (CI/CD): Establish CI/CD pipelines for automated testing, validation, and deployment of ML models using tools like Jenkins, GitLab CI/CD, or CircleCI.Implement version control and model versioning practices to manage changes and updates to ML models.
Security and Governance: Implement security best practices for securing ML infrastructure, data, and models in compliance with regulatory requirements. Establish governance policies and access controls for managing and monitoring ML artifacts and resources.
Training and Knowledge Sharing: Provide training and mentorship to data scientists, engineers, and stakeholders on MLOps practices, tools, and methodologies.
Foster a culture of collaboration and continuous improvement in MLOps adoption across the organization.
Client Engagement and Consulting: Work closely with clients to understand their MLOps needs, assess their current ML infrastructure, and recommend solutions for MLOps implementation. Provide clients strategic guidance and technical expertise in adopting MLOps practices and optimizing their ML deployment pipelines.
Strong proficiency in programming languages such as Python, Java, or Scala, and experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
Experience with cloud platforms (AWS, Azure, Google Cloud) and containerization technologies (Docker, Kubernetes) for deploying ML workloads.
Familiarity with CI/CD pipelines, version control systems (e.g., Git), and automation tools for managing ML infrastructure and deployments.
Knowledge of MLOps tools and platforms such as Apache Airflow, MLflow, Kubeflow, or similar.
Understanding of security, governance, and compliance requirements in deploying and managing ML models in production.



We work closely with

Java Script
ML Ops
CI/CD
ECS/ECR
Jenkins
REST APIs
GitLab
Python
Jfrom
TensorFlow
AWS
Python
Java
UNIX
Google Cloud



Our Process

Schedule a 15 min Video Call with someone from our Team
4 Proctored GQ Tests (30-45 min Final Video Interview
Receive Job Offer


If you are interested in reaching out to us, please apply and our team will contact you within the hour.